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Record W4311270870 · doi:10.1088/1758-5090/acab35

Bioprinting of alginate-carboxymethyl chitosan scaffolds for enamel tissue engineering in vitro

2022· article· en· W4311270870 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBiofabrication · 2022
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsCanadian Light Source (Canada)University of Saskatchewan
FundersCanadian Institutes of Health Research
Keywords3D bioprintingMaterials scienceScaffoldSwellingTissue engineeringSelf-healing hydrogelsBiomedical engineeringRegeneration (biology)ChitosanChemical engineeringComposite materialPolymer chemistry

Abstract

fetched live from OpenAlex

Abstract Tissue engineering offers a great potential in regenerative dentistry and to this end, three dimensional (3D) bioprinting has been emerging nowadays to enable the incorporation of living cells into the biomaterials (such a mixture is referred as a bioink in the literature) to create scaffolds. However, the bioinks available for scaffold bioprinting are limited, particularly for dental tissue engineering, due to the complicated, yet compromised, printability, mechanical and biological properties simultaneously imposed on the bioinks. This paper presents our study on the development of a novel bioink from carboxymethyl chitosan (CMC) and alginate (Alg) for bioprinting scaffolds for enamel tissue regeneration. CMC was used due to its antibacterial ability and superior cell interaction properties, while Alg was added to enhance the printability and mechanical properties as well as to regulate the degradation rate. The bioinks with three mixture ratios of Alg and CMC (2–4, 3–3 and 4–2) were prepared, and then printed into the calcium chloride crosslinker solution (100 mM) to form a 3D structure of scaffolds. The printed scaffolds were characterized in terms of structural, swelling, degradation, and mechanical properties, followed by their in vitro characterization for enamel tissue regeneration. The results showed that the bioinks with higher concentrations of Alg were more viscous and needed higher pressure for printing; while the printed scaffolds were highly porous and showed a high degree of printability and structural integrity. The hydrogels with higher CMC ratios had higher swelling ratios, faster degradation rates, and lower compressive modulus. Dental epithelial cell line, HAT-7, could maintain high viability in the printed constructs after 1, 7 and 14 d of culture. HAT-7 cells were also able to maintain their morphology and secrete alkaline phosphatase after 14 d of culture in the 3D printed scaffolds, suggesting the capacity of these cells for mineral deposition and enamel-like tissue formation. Among all combinations Alg4%–CMC2% and in a less degree 2%Alg–4%CMC showed the higher potential to promote ameloblast differentiation, Ca and P deposition and matrix mineralization in vitro . Taken together, Alg-CMC has been illustrated to be suitable to print scaffolds with dental epithelial cells for enamel tissue regeneration.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score0.519

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.254
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it